Wireless signal receiver with maximum likelihood detector

ABSTRACT

The present invention discloses an ML (Maximum Likelihood) detector. An embodiment of the ML detector comprises a search value selecting circuit and an ML detecting circuit. The search value selecting circuit is configured to select a first-layer search value. The ML detecting circuit is configured to carry out the following steps: selecting first-layer candidate values according to the first-layer search value, one of a reception signal and a derivative thereof, and one of a channel estimation signal and a derivative thereof, and adding one or more first-layer candidate value(s), if necessary; calculating second-layer candidate values according to all the above-mentioned first-layer candidate values, and adding one or more second-layer candidate value(s) and its/their corresponding first-layer candidate value(s), if necessary; and calculating log likelihood ratios according to the whole first-layer and second-layer candidate values.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to wireless signal reception technique,especially to wireless signal reception technique using maximumlikelihood detection.

2. Description of Related Art

Nowadays wireless communication technique is widely spread and usershave more and more demand for data throughput. Therefore, this industryfield keeps studying how to increase bandwidth usage efficiency underlimited system bandwidth and raise system throughput. A market trend isusing a spatial multiplexing transmission manner of multiple-inputmultiple-output (MIMO) technique which can greatly raise systemthroughput without engaging more bandwidth. This transmission mannergets a lot of attention recently.

The spatial multiplexing transmission manner uses a plurality ofantennas of a transmitter to transmit signals independent of one anotherthrough the same frequency band at the same time, while a receiver usesa plurality of antennas to receive and detect these signals. In order toachieve better demodulation efficiency, the receiver may use a maximumlikelihood algorithm (ML algorithm). The ML algorithm could beunderstood as an algorithm in search of an optimum solution. The MLalgorithm makes use of an exhaustive search manner to estimate anoptimum solution of transmission signals among all possible solutionsaccording to reception signals. However, the exhaustive search manner isnot an efficient manner because it computes all of the possiblesolutions and takes a long time to measure processing latency,complexity and computation power.

People who are interested in the prior arts may refer to the followingliteratures:

-   (1) China patent application of publication number CN101582748A; and-   (2) Massimiliano Siti, Michael P. Fitz, “A Novel Soft-Output Layered    Orthogonal Lattice Detector for Multiple Antenna Communications”,    IEEE International Conference on Communications (ICC), 2006.

SUMMARY OF THE INVENTION

In consideration of the problems of the prior arts, an object of thepresent invention is to provide a maximum likelihood detector for makingimprovements over the prior arts.

The present invention discloses a maximum likelihood detector (MLdetector) configured to execute a maximum likelihood detection (MLdetection) according to one of a reception signal and a derivativethereof and according to one of a channel estimation signal and aderivative thereof. An embodiment of the ML detector comprises: a searchvalue selecting circuit configured to determine a first-layer searchvalue; and a maximum likelihood detecting circuit (ML detectingcircuit). The ML detecting circuit is configured to execute at least thefollowing steps: selecting K first-layer candidate signal valuesaccording to the first-layer search value, one of the reception signaland the derivative thereof, and one of the channel estimation signal andthe derivative thereof, in which the K is an integer greater than one;determining whether to add Q first-layer candidate signal value(s)according to the K first-layer candidate signal values and therebygenerating a first decision result, in which the Q is a positiveinteger; when the first decision result is affirmative, adding the Qfirst-layer candidate signal value(s), and calculating (K+Q)second-layer candidate signal values according to (K+Q) first-layercandidate signal values composed of the K and the Q first-layercandidate signal values; when the first decision result is affirmative,determining whether to add P second-layer candidate signal value(s)according to the (K+Q) second-layer candidate signal values and therebygenerating a second decision result, in which the P is a positiveinteger; when the first and the second decision results are affirmative,adding the P second-layer candidate signal value(s), selecting Pfirst-layer candidate signal value(s) according to the P second-layercandidate signal value(s), and calculating log likelihood ratios (LLRs)according to (K+Q+P) first-layer candidate signal values composed of theK, the Q and the P first-layer candidate signal values and according to(K+Q+P) second-layer candidate signal values composed of the (K+Q) the Psecond-layer candidate signal values; when the first decision result isaffirmative and the second decision result is negative, calculating LLRsaccording to the (K+Q) first-layer candidate signal values and the (K+Q)second-layer candidate signal values; when the first decision result isnegative, calculating K second-layer candidate signal values accordingthe K first-layer candidate signal values; when the first decisionresult is negative, determining whether to add R second-layer candidatesignal value(s) according to the K second-layer candidate signal valuesand thereby generating a third decision result, in which the R is apositive integer; when the first decision result is negative and thethird decision result is affirmative, adding the R second-layercandidate signal value(s), selecting R first-layer candidate signalvalue(s) according to the R second-layer candidate signal value(s), andcalculating LLRs according to (K+R) first-layer candidate signal valuescomposed of the K and the R first-layer candidate signal values andaccording to (K+R) second-layer candidate signal values composed of theK and the R second-layer candidate signal values; and when the first andthe third decision results are negative, calculating LLRs according tothe K first-layer candidate signal values and the K second-layercandidate signal values.

Another embodiment of the aforementioned ML detector comprises: a searchvalue selecting circuit configured to determine a first search value anda second search value; and an ML detecting circuit. The ML detectingcircuit is configured to execute at least the following steps: selectingK₁ first-layer candidate signal values according to the first searchvalue, one of the reception signal and the derivative thereof, and oneof the channel estimation signal and the derivative thereof, in whichthe K₁ is an integer greater than one; determining whether to add Qfirst-layer candidate signal value(s) according to the K₁ first-layercandidate signal values and thereby generating a first decision result,in which the Q is a positive integer; when the first decision result isaffirmative, adding the Q first-layer candidate signal value(s), andcalculating (K₁+Q) second-layer candidate signal values according to(K₁+Q) first-layer candidate signal values composed of the K₁ and the Qfirst-layer candidate signal values; when the first decision result isaffirmative, calculating log likelihood ratios (LLRs) according to the(K₁+Q) first-layer candidate signal values and the (K₁+Q) second-layercandidate signal values; when the first decision result is negative,calculating K₁ second-layer candidate signal values according to the K₁first-layer candidate signal values and calculating LLRs according tothe K₁ first-layer candidate signal values and K₁ second-layer candidatesignal values; selecting K₂ first-layer candidate signal valuesaccording to the second search value, one of the reception signal andthe derivative thereof, and one of a swap signal of the channelestimation signal and a derivative of the swap signal, in which the K₂is an integer greater than one; determining whether to add P first-layercandidate signal value(s) according to the K₂ first-layer candidatesignal values and thereby generating a second decision result, in whichthe P is a positive integer; when the second decision result isaffirmative, adding the P first-layer candidate signal value(s), andcalculating (K₂+P) second-layer candidate signal values according to(K₂+P) first-layer candidate signal values composed of the K₂ and the Pfirst-layer candidate signal values; when the second decision result isaffirmative, calculating LLRs according to the (K₂+P) first-layercandidate signal values and the (K₂+P) second-layer candidate signalvalues; when the second decision result is negative, calculating K₂second-layer candidate signal values according to the K₂ first-layercandidate signal values and calculating LLRs according to the K₂first-layer candidate signal values and K₂ second-layer candidate signalvalues.

These and other objectives of the present invention will no doubt becomeobvious to those of ordinary skill in the art after reading thefollowing detailed description of the preferred embodiments that areillustrated in the various figures and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an embodiment of the wireless signal receiver with amaximum likelihood detection function according to the presentinvention.

FIG. 2 shows a tree diagram of exhaustive search.

FIG. 3 shows a tree diagram under a configuration of a Layer OrthogonalLattice Detector.

FIG. 4 illustrates an embodiment of the maximum likelihood detector ofthe present invention.

FIG. 5 illustrates an embodiment of the signal detecting circuit of FIG.4.

FIG. 6 illustrates an exemplary operation, which is executed by themaximum likelihood detector of FIG. 4, with a constellation diagram.

FIG. 7 shows a tree diagram associated with FIG. 6.

FIG. 8 illustrates an embodiment of the signal detecting circuit of FIG.4.

FIG. 9 illustrates an example of insufficient first-layer candidatesignal values.

FIG. 10 illustrates an exemplary implementation concept of the candidatesignal value adding unit of FIG. 8.

FIG. 11 illustrates an embodiment of the signal detecting circuit ofFIG. 4.

FIG. 12 illustrates an embodiment of the signal detecting circuit ofFIG. 4.

FIG. 13 illustrates an embodiment of the first detecting circuit of FIG.12.

FIG. 14 illustrates an embodiment of the second detecting circuit ofFIG. 12.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description is written by referring to terms acknowledgedin this industrial field. If any term is defined in this specification,such term should be explained accordingly.

The present disclosure includes a maximum likelihood detector (MLdetector), a maximum likelihood detecting method (ML detecting method)and a wireless signal receiver with a maximum likelihood detectionfunction (ML detection function) configured to operate by bit in themanner of soft decision to output a log likelihood ratio (LLR) to adecoding circuit for error correction. On account of that some elementof the ML detector and the wireless signal receiver of the presentdisclosure could be known, the detail of such element will be omitted,provided that this omission has little to do with the writtendescription and enablement requirements. Besides, the method of thepresent disclosure can be in the form of firmware and/or software whichcould be carried out by the hardware of the present disclosure or theequivalent thereof. The present invention is applicable tomultiple-dimension or multiple-layer signal reception such as signalreception of a multiple-input multiple output (MIMO) communicationsystem. Communication technique using MIMO, for example, includesLong-Term Evolution (LTE) technique, Wireless Local-Area Network (WLAN)technique, Worldwide Interoperability for Microwave Access (WiMax)technique, etc. For the convenience of understanding, the embodiments inthis specification are described in accordance with applications of LTEcommunication system; however, the present invention can be applied toother kinds of communication systems.

Please refer to FIG. 1. FIG. 1 illustrates an embodiment of the wirelesssignal receiver with the ML detection function according to the presentinvention. The wireless signal receiver 100 of FIG. 1 comprises: aDiscrete Fourier Transform (DFT) circuit 110 configured to convert atime-domain signal into a frequency-domain signal; a reference signal(i.e., ref. signal in FIG. 1) extraction circuit 120 configured togenerate an extraction reference signal according to thefrequency-domain signal; a channel estimation circuit 130 configured togenerate an estimation signal according to the extraction referencesignal; a data signal extraction circuit 140 configured to generate anextraction data signal according to the frequency-domain signal; asignal detecting circuit 150 configured to generate a detection signalaccording to the channel estimation signal and the extraction datasignal; and a decoding circuit 160 configured to generate a decodedsignal according to the detection signal. The signal detecting circuit150 includes a maximum likelihood detector (ML detector) 152 configuredto execute at least the following steps: determining a first-layersearch value; selecting K first-layer candidate signal values accordingto the first-layer search value, one of the reception signal and thederivative thereof, and one of the channel estimation signal and thederivative thereof, in which the K is an integer greater than one;determining whether to add Q first-layer candidate signal value(s)according to the K first-layer candidate signal values and therebygenerating a first decision result, in which the Q is a positiveinteger; when the first decision result is affirmative, adding the Qfirst-layer candidate signal value(s), and calculating (K+Q)second-layer candidate signal values according to (K+Q) first-layercandidate signal values composed of the K and the Q first-layercandidate signal values; when the first decision result is affirmative,determining whether to add P second-layer candidate signal value(s)according to the (K+Q) second-layer candidate signal values and therebygenerating a second decision result, in which the P is a positiveinteger; when the first and the second decision results are affirmative,adding the P second-layer candidate signal value(s), selecting Pfirst-layer candidate signal value(s) according to the P second-layercandidate signal value(s), and calculating log likelihood ratios (LLRs)according to (K+Q+P) first-layer candidate signal values composed of theK, the Q and the P first-layer candidate signal values and according to(K+Q+P) second-layer candidate signal values composed of the (K+Q) the Psecond-layer candidate signal values; when the first decision result isaffirmative and the second decision result is negative, calculating LLRsaccording to the (K+Q) first-layer candidate signal values and the (K+Q)second-layer candidate signal values; when the first decision result isnegative, calculating K second-layer candidate signal values accordingthe K first-layer candidate signal values; when the first decisionresult is negative, determining whether to add R second-layer candidatesignal value(s) according to the K second-layer candidate signal valuesand thereby generating a third decision result, in which the R is apositive integer; when the first decision result is negative and thethird decision result is affirmative, adding the R second-layercandidate signal value(s), selecting R first-layer candidate signalvalue(s) according to the R second-layer candidate signal value(s), andcalculating LLRs according to (K+R) first-layer candidate signal valuescomposed of the K and the R first-layer candidate signal values andaccording to (K+R) second-layer candidate signal values composed of theK and the R second-layer candidate signal values; and when the first andthe third decision results are negative, calculating LLRs according tothe K first-layer candidate signal values and the K second-layercandidate signal values. The above-mentioned Q first-layer candidatesignal value(s), P second-layer candidate signal value(s) and Rsecond-layer candidate signal value(s) can be called supplementalcandidate signal values. An embodiment of the decoding circuit 160includes a descrambler and a turbo decoder, in which the descrambler isconfigured to generate a descrambled signal according to the detectionsignal and the turbo decoder is configured to generate the decodedsignal according to the descrambled signal. Each of the DFT circuit 110,the reference signal extraction circuit 120, the channel estimationcircuit 130, the data signal extraction circuit 140 and the decodingcircuit 160 itself is a known or self-developed circuit.

On the basis of the above, the time-domain signal is converted into thefrequency-domain signal through DFT, and the extraction data signalY_(n) of the n^(th) sub-carrier in the frequency domain can be expressedby a N_(R)×1 vector as shown below:Y _(n) =H _(n) X _(n) +W _(n)  (Eq. 1),in which H_(n) stands for a N_(R)×N_(T) channel matrix from the n^(th)sub-carrier's point of view, X_(n) stands for the N_(T)×1 transmissionsignal of the n^(th) sub-carrier, W_(n) stands for the n^(th)sub-carrier's noise, N_(R) is the number of reception antenna(s), andN_(T) is the number of transmission antenna(s). This embodiment takesthe signal of a sub-carrier as a to-be-processed unit; for theconvenience of expression, in the following description, the suffix n isomitted. In addition, this embodiment relates to an application oftwo-transmission two-reception spatial multiplexing transmission, thatis to say both the number of independent spatial streams at atransmission end and the number of independent spatial streams at areception end being two (and each of N_(R) and N_(T) being not less thantwo). Accordingly, Eq. 1 can be simplified as follows:Y=HX+W  (Eq. 2)

In an LTE system, a reference signal (RS) and a data signal are carriedby different sub-carriers, and thus the channel estimation circuit 130derives a channel response matrix Ĥ of a target data signal based onchannel estimation, which is estimated according to the referencesignal, in the manner of interpolation or extrapolation; then the signaldetecting circuit 150 performs QR decomposition or the equivalentthereof to the matrix Ĥ as shown below:Ĥ=QR  (Eq. 3),in which Q is a unitary matrix and R is an upper triangular matrix.After the QR decomposition or the equivalent thereof is done, the signaldetecting circuit 150 multiplies the reception signal Y (i.e., theaforementioned extraction data signal) by Q^(H) (i.e., Hermitian matrixof matrix Q) or executes an equivalent calculation to obtain a signal Zas follows:Z≡Q ^(H) Y=Q ^(H)(HX+W)=Q ^(H)(QRX+W)=RX+W′  (Eq. 4),in which W′≡Q^(H)W. Afterward, the ML detector 152 calculates a loglikelihood ratio (LLR) by bit according to the signal Z of Eq. 4. Thecalculation could be expressed as follows:

$\begin{matrix}{{{L( b_{i} \middle| Y )} = {{\min\limits_{\overset{\sim}{X} \in {G{(X_{b_{i} = 1})}}}{{Z - {R\overset{\sim}{X}}}}^{2}} - {\min\limits_{\overset{\sim}{X} \in {G{(X_{b_{i} = 0})}}}{{Z - {R\overset{\sim}{X}}}}^{2}}}},} & ( {{Eq}.\mspace{11mu} 5} )\end{matrix}$in which b₁ stands for i^(th) bit, G(X_(b) _(i) ₌₀) stands for a groupof transmission signal(s) whose i^(th) bit is/are 0, G(X_(b) _(i) ₌₁)stands for a group of transmission signal(s) whose i^(th) bit is/are 1,{tilde over (X)} stands for candidate signal value(s) of the calculationprocess, and the symbol “˜” is used to distinguish {tilde over (X)} froma real signal X.

Eq. 5 can be regarded as an equation in search of an optimum solution,and the optimum solution can be found through exhaustive search asmentioned in the description of related art. If the modulation type of atransmission signal is M-QAM (i.e., quadrature amplitude modulation witha signal set of size M (which implies that the number of constellationpoints in a constellation diagram corresponding to such modulation typeis M)), for two-layer independent data streams of two-transmissiontwo-reception, the number of possible solutions of Eq. 5 (i.e.,candidate signal value(s)) is M² which can be illustrated by a treediagram as shown in FIG. 2.

In detail, since R is an upper triangular matrix, the operation insearch of

$\min\limits_{\overset{\sim}{X}}{{Z - {R\overset{\sim}{X}}}}^{2}$can be simplified. Firstly,

$\min\limits_{\overset{\sim}{X}}{{Z - {R\overset{\sim}{X}}}}^{2}$can be decomposed as follows:

$\begin{matrix}\begin{matrix}{{\min\limits_{\overset{\sim}{X}}{{Z - {R\overset{\sim}{X}}}}^{2}} = {\min\limits_{\overset{\sim}{X}}{{\begin{bmatrix}Z_{0} \\Z_{1}\end{bmatrix} - {\begin{bmatrix}R_{00} & R_{01} \\0 & R_{11}\end{bmatrix}\begin{bmatrix}{\overset{\sim}{X}}_{0} \\{\overset{\sim}{X}}_{1}\end{bmatrix}}}}^{2}}} \\{= {\min\limits_{\overset{\sim}{X}}\lbrack {{{Z_{0} - {R_{00}{\overset{\sim}{X}}_{0}} - {R_{01}{\overset{\sim}{X}}_{1}}}}^{2} + {{Z_{1} - {R_{11}{\overset{\sim}{X}}_{1}}}}^{2}} \rbrack}}\end{matrix} & ( {{Eq}.\mspace{11mu} 6} )\end{matrix}$Since the square of an absolute value is not less than zero, ∥Z−R{tildeover (X)}∥² will be the minimum with a selected {tilde over (X)}₁ whenthe following equation is true:

$\begin{matrix}{{{\overset{\sim}{X}}_{0} = {\Gamma\lbrack \frac{Z_{0} - {R_{01}{\overset{\sim}{X}}_{1}}}{R_{00}} \rbrack}},} & ( {{Eq}.\mspace{11mu} 7} )\end{matrix}$in which Γ[ ] stands for a quantizer. In other words, after {tilde over(X)}₁ is selected, the one and only solution derived from Eq. 7 is{tilde over (X)}₀. The configuration associated with Eq. 6 and Eq. 7 iscalled a Layer Orthogonal lattice Detector (LOAD). Under suchconfiguration, a tree diagram for search can be greatly simplified asshown in FIG. 3. In a LOAD configuration corresponding to FIG. 3, at thefirst layer are unfolded all possible solutions (M possible solutions)of {tilde over (X)}₁, and at the second layer is derived {tilde over(X)}₀ according to Eq. 7 directly; accordingly, there is no need tounfold all possible solutions of {tilde over (X)}₀ in comparison withFIG. 2. As a result, the whole computation complexity is up to thenumber of possible solutions of {tilde over (X)}₁ unfolded at the firstlayer.

A log likelihood ratio (LLR) of every bit can be obtained through thecalculation of Eq. 5 associated with the LOAD configurationcorresponding to FIG. 3, which implies that a number of the LLRs isequal to a number of the bits of the transmission signal; afterwardthese LLRs are inputted to a decoder for error correction so as tocomplete a reception process. However, take M-QAM; the number ofpossible solutions (i.e., the number of {tilde over (X)}₁ unfolded atthe first layer in FIG. 3) is M, and some of the possible solutionstherein is unlikely to be correct and better to be removed fromconsideration for simplifying calculation. Therefore, the ML detector152 of FIG. 1 does not calculate LLRs of the M possible solutions, butselects/determines a search value first and then executes an MLcalculation according to the search value and one of thefrequency-domain signal and the derivative thereof (e.g., theaforementioned extraction data signal) to generate the detection signalincluding LLR(s) as described in the preceding paragraph, in which thesearch value is associated with a search range, and the number ofcandidate signal value(s) in the search range (that is to say the numberof constellation points within the search range in a constellationdiagram corresponding to the modulation type) is not greater than thenumber of all candidate signal values of the modulation type (that is tosay the number of all constellation points in the constellation diagramcorresponding to the modulation type). In other words, the ML detector152 in FIG. 1 unfolds only K possible solution(s) (i.e., K candidatesignal values) of {tilde over (X)}₁ (or {tilde over (X)}₀) at the firstlayer, and since K≦M, lower computation complexity and power consumptioncan be realized without substantially reducing performance. It should benoted that in a reversed operation, the ML detector 152 may unfoldpossible solutions of {tilde over (X)}₀ at the first layer with the aidof a swap operation decision unit (which will be explained in thefollowing paragraphs) and then unfold possible solutions of {tilde over(X)}₁ at the second layer. For the ease of understanding, most of theembodiments in the following description adopt a straight operationwhich unfolds possible solutions of {tilde over (X)}₁ at the first layerand then unfolds possible solutions of {tilde over (X)}₀ at the secondlayer. However, the reversed operation may be adopted according toimplementer's discretion, and can be easily derived from the disclosureof the straight operation in this specification.

In detail, an embodiment of the ML detector 152 is shown in FIG. 4,including a search value selecting circuit 410 and a maximum likelihooddetecting circuit (ML detecting circuit) 420. The search value selectingcircuit 410 is configured to determine the aforementioned first-layersearch value. An example of the first-layer search value is not lessthan a predetermined threshold. A number of the first-layer candidatesignal value(s), associated with the predetermined threshold, is notgreater than a number of all first-layer candidate signal values of themodulation type. For instance, when the modulation type is M-QAM, anon-restrictive example of the first-layer search value is an integergreater than M/4, and another non-restrictive example of the first-layersearch value is an integer between 4 and (√{square root over (M)}−1)².The ML detecting circuit 420 is configured to execute the calculation ofEq. 8 and a look-up-table operation, which are described in thefollowing paragraphs, to obtain the first-layer candidate signalvalue(s), and configured to execute the calculation of theaforementioned Eq. 7 to obtain the second-layer candidate signalvalue(s), and further configured to execute the calculation of theaforementioned Eq. 5 to generate the LLRs.

An exemplary implementation of the signal detecting circuit 150 of FIG.4 is shown in FIG. 5, including: a QR decomposition unit 530 configuredto execute the calculation of Eq. 3 or the equivalent thereof accordingto the aforementioned estimation signal (associated with or equal to thechannel response matrix Ĥ); a signal generating unit 540 configured toexecute the calculation of Eq. 4 or the equivalent thereof according tothe aforementioned extraction data signal (associated with or equal tothe aforementioned signal Y); the search value selecting circuit 410;and the ML detecting circuit 420. In FIG. 5, the ML detecting circuit420 includes: a first-layer candidate signal value decision unit 522configured to execute the calculation of Eq. 8 or the equivalent thereofaccording to the extraction data signal so as to generate a computationresult, and configured to execute the following look-up-table operationor the equivalent thereof according to the computation result and thefirst-layer search value so as to generate an operation result; and asecond-layer candidate signal value decision unit 524 configured toexecute the calculation of Eq. 7 or the equivalent thereof according tothe operation result so as to generate a calculation result includingthe operation result; and an LLR calculation unit 526 configured toexecute the calculation of Eq. 5 or the equivalent thereof according tothe calculation result. Each of the said QR decomposition unit 530 andsignal generating unit 540 itself is a known or self-developed unit.

The ML detecting circuit 420 selects the first-layer search value, whichis associated with the search range, according to the pinpoint of areception signal mapped on a constellation diagram corresponding to themodulation type. Based on Eq. 6, an exemplary implementation of the MLdetecting circuit 420 includes a zero-forcing (ZF) equalizer, which isincluded in the candidate signal value decision unit 422, to execute aZF calculation as shown below for determining the pinpoint {tilde over(X)}_(1,eq) of {tilde over (X)}₁ mapped on a constellation diagram:

$\begin{matrix}{{\overset{\sim}{X}}_{1,{eq}} = \frac{Z_{1}}{R_{11}}} & ( {{Eq}.\mspace{11mu} 8} )\end{matrix}$In light of the feature of a constellation diagram, the distance (thatis to say difference) between each constellation point of theconstellation diagram and the pinpoint {tilde over (X)}_(1,eq) can bedetermined in advance.

Take 64-QAM of LTE for example. Fourteen sections can be defined asshown in Table 1 below according to the relation between the locationsof the pinpoint {tilde over (X)}_(1,eq) (i.e., a reference valueassociated with the pinpoint {tilde over (X)}_(1,eq)) and theconstellation points. In each section of Table 1, the left-to-rightorder indicates that the distance between a constellation point in thesection and the pinpoint {tilde over (X)}_(1,eq) gets farther by theorder. With the constellation diagram of FIG. 6, the pinpoint {tildeover (X)}_(1,eq) of {tilde over (X)}₁ mapped on the constellationdiagram is clearly shown, in which the symbol “x” of the digits“1x1x1x”, “x0x1x1”, etc. can be zero or one. In the example of FIG. 6,the real-part of {tilde over (X)}_(1,eq) is located between the section2≦S≦3 and the real-parts of the constellation points in the section bythe close-to-far order (corresponding to the aforementionedleft-to-right order) are 3, 1, 5, −1, 7, −3, −5 and −7 which amount toeight possible solutions. Similarly, the imaginary-part of {tilde over(X)}_(1,eq) is located between the section −3≦S≦−2 and theimaginary-parts of the constellation points in the section by theclose-to-far order are −3, −1, −5, 1, −7, 3, 5 and 7 which amount toeight possible solutions. Assuming that the search valueselected/determined by the search value selecting circuit 410 istwenty-five (which implies that only twenty-five possible solutions of{tilde over (X)}₁ (or {tilde over (X)}₀) are unfolded by the MLdetecting circuit 420 at the first layer), the ML detecting circuit 420will pick five real-parts closest to the pinpoint {tilde over(X)}_(1,eq) and five imaginary-parts closest to the pinpoint {tilde over(X)}_(1,eq) to obtain 5×5=25 possible solutions (i.e., 25 first-layercandidate signal values) which can be understood as a search range(e.g., a range in a shape of rectangle or another form) surrounding thepinpoint {tilde over (X)}_(1,eq) (i.e., the center) in FIG. 6, and allconstellation points within this search range are the constellationpoints (e.g., those closest to the pinpoint {tilde over (X)}_(1,eq))calculated by the ML detecting circuit 420 when unfolding {tilde over(X)}₁ at the first layer; meanwhile, the constellation points withoutthe search range are removed from consideration. Accordingly, thepresent embodiment can lower computation complexity as shown in the treediagram of FIG. 7. It should be noted that the present embodiment mayobtain the combination of the picked real-parts and imaginary-parts(i.e., the said possible solutions) through a look-up-table operationaccessing pre-stored data (e.g., the data of Table 1), so as to make theML detecting circuit 420 calculate LLR(s) according to Eq. 5 moreefficiently.

TABLE 1    S <−6 −7 −5 −3 −1 1 3 5 7 −6 ≦ S < −5 −5 −7 −3 −1 1 3 5 7 −5≦ S < −4 −5 −3 −7 −1 1 3 5 7 −4 ≦ S < −3 −3 −5 −1 −7 1 3 5 7 −3 ≦ S < −2−3 −1 −5 1 −7 3 5 7 −2 ≦ S < −1 −1 −3 1 −5 3 −7 5 7 −1 ≦ S < 0  −1 1 −33 −5 5 −7 7 0 ≦ S < 1 1 −1 3 −3 5 −5 7 −7 1 ≦ S < 2 1 3 −1 5 −3 7 −5 −72 ≦ S < 3 3 1 5 −1 7 −3 −5 −7 3 ≦ S < 4 3 5 1 7 −1 −3 −5 −7 4 ≦ S < 5 53 7 1 −1 −3 −5 −7 5 ≦ S < 6 5 7 3 1 −1 −3 −5 −7 6 ≦ S   7 5 3 1 −1 −3 −5−7

Referring to FIG. 6, it is obvious that the scope of the search rangehas dominant influence on the computation complexity. When the searchrange includes all constellation points (i.e., sixty-four constellationpoints in FIG. 6), the computation complexity of the present embodimentis equivalent to the computation complexity of the aforementioned LORDconfiguration. In other words, the computation complexity of the presentembodiment is equal to or less than the computation complexity of theLORD configuration. Of course the scope of the search range can be setin advance, and can be modified according to a communication status.When the scope of the search range is set in advance, the first-layersearch value is determined by the search value selecting circuit 410 inaccordance with the aforementioned predetermined value; for instance,the search value is equal to the predetermined value. When the scope ofthe search range is set according to the communication status, the indexof the communication status (i.e., the aforementioned communicationindex) could be associated with at least one of a signal-to-noise ratio,sub-carrier reception signal energy strength, channel energy strength,channel correlation, channel estimation precision and interferenceenergy strength, etc.

On the basis of the above, when the scope of the search range isdetermined according to the communication status, the scope of thesearch range, for example, could be determined according to thesignal-to-noise ratio (SNR) during the ML detecting circuit 420executing the ZF calculation for determining the pinpoint {tilde over(X)}_(1,eq) of {tilde over (X)}₁ on the constellation diagram. The saidSNR can be represented by the symbol γ, and expressed as follows:

$\begin{matrix}{\gamma = \frac{{R_{11}}^{2}}{\sigma_{W}^{2}}} & ( {{Eq}.\mspace{11mu} 9} )\end{matrix}$in which σ_(W) ² stands for noise energy which can be measured in aconventional way known in this field, and R₁₁ is one of the elements ofthe aforementioned upper triangular matrix R. The greater the γ, themore reliable the ZF calculation result of the ML detecting circuit 420;in this case, the search value selecting circuit 410 may select asmaller search value (i.e., smaller search range). On the other hand,the smaller the γ, the greater the search value (i.e., the greatersearch range) that the search value selecting circuit 410 may choose. Inlight of the above, the present embodiment may define one or morethreshold(s) T_(g) (g=1, 2, . . . , G), in which T_(g)<T_(g+1). As shownin Table 2, when γ is greater than some threshold T_(g) (e.g., T₁), thesearch value selecting circuit 410 will adopt a smaller search value(e.g., K₂), in which K_(G+1)<K_(G)< . . . <K₂<K₁.

TABLE 2 γ < T₁ K₁ T₁ ≦ γ < T₂ K₂ . . . . . . T_(g) ≦ γ < T_(g+1) K_(g) .. . . . . T_(G) ≦ γ K_(G+1)

In addition to the SNR (as shown in Eq. 9), the search value selectingcircuit 410 is operable to select the search value (i.e., search range)according to other communication indexes. For instance, the search valueselecting circuit 410 may select the search value according to channelcorrelation ρ which can be expressed as follows:

$\begin{matrix}{\rho = \frac{h_{1}^{H}h_{2}}{\sqrt{{h_{1}}_{2}^{2}}\sqrt{{h_{2}}_{2}^{2}}}} & ( {{Eq}.\mspace{11mu} 10} )\end{matrix}$

In Eq. 10, h₁ stands for the first column of the channel response matrixĤ and h₂ stands for the second column of the matrix Ĥ. The greater the ρ(i.e., the smaller the 1/ρ), the more unreliable the ZF calculationresult of {tilde over (X)}₁ calculated by the ML detecting circuit 420;in this case, the search value selecting circuit 410 may select agreater search value (i.e., greater search range). On the other hand,the smaller the ρ (i.e., the greater the 1/ρ), the smaller the searchvalue (i.e., the smaller search range) that the search value selectingcircuit 410 may choose. Therefore, when the communication index (e.g.,γ, 1/ρ, etc.) is above a first threshold, the first-layer search valueis a first value; when the communication index is below the firstthreshold, the first-layer search value is a second value. The firstvalue is smaller than the second value, and the communication statusrepresented by the communication index above the first threshold isbetter than the communication status represented by the communicationindex below the first threshold.

On the basis of the above, when the first-layer search value is notgreat enough, in the search range a set of n^(th) bits of allfirst-layer candidate signal values will lack a set of bit(s) being “0”or “1” as shown in FIG. 9; in the meantime the ML detector 152 will findthe set of bit(s) being “0” or “1” is null (i.e., an empty set) whencalculating the LLRs of Eq. 5, and such empty set will cause the MLdetector 152 to fail to derive correction information when calculatingthe LLRs (because the concept of LLR calculation is to compare theprobability of bit(s) being “1” with the probability of bit(s) being “0”for every bit set (i.e., every set of n^(th) bits)), so that an extracomputation process is necessary and performance loss is expected.Therefore, as it is mentioned above, when the first-layer search valueis not great enough, after the K first-layer candidate signal values areobtained in accordance with the first-layer search value, the MLdetector 152 may further execute the following steps: determiningwhether to add Q first-layer candidate signal value(s) according to theK first-layer candidate signal values and thereby generating a firstdecision result, in which the Q is a positive integer; when the firstdecision result is affirmative, adding the Q first-layer candidatesignal value(s), and calculating (K+Q) second-layer candidate signalvalues according to (K+Q) first-layer candidate signal values composedof the K and the Q first-layer candidate signal values; when the firstdecision result is affirmative, determining whether to add Psecond-layer candidate signal value(s) according to the (K+Q)second-layer candidate signal values and thereby generating a seconddecision result, in which the P is a positive integer; when the firstand the second decision results are affirmative, adding the Psecond-layer candidate signal value(s), selecting P first-layercandidate signal value(s) according to the P second-layer candidatesignal value(s), and calculating log likelihood ratios (LLRs) accordingto (K+Q+P) first-layer candidate signal values composed of the K, the Qand the P first-layer candidate signal values and according to (K+Q+P)second-layer candidate signal values composed of the (K+Q) the Psecond-layer candidate signal values; and when the first decision resultis affirmative and the second decision result is negative, calculatingLLRs according to the (K+Q) first-layer candidate signal values and the(K+Q) second-layer candidate signal values. It should be noted that whenthe first decision result is negative (that is to say the first-layersearch value great enough), the effect of such result is the Q beingequal to zero. Accordingly, those of ordinary skill in the art canappreciate how to execute the following steps by the ML detector 152:when the first decision result is negative, calculating K second-layercandidate signal values according the K first-layer candidate signalvalues; when the first decision result is negative, determining whetherto add R second-layer candidate signal value(s) according to the Ksecond-layer candidate signal values and thereby generating a thirddecision result, in which the R is a positive integer; when the firstdecision result is negative and the third decision result isaffirmative, adding the R second-layer candidate signal value(s),selecting R first-layer candidate signal value(s) according to the Rsecond-layer candidate signal value(s), and calculating LLRs accordingto (K+R) first-layer candidate signal values composed of the K and the Rfirst-layer candidate signal values and according to (K+R) second-layercandidate signal values composed of the K and the R second-layercandidate signal values; and when the first and the third decisionresults are negative, calculating LLRs according to the K first-layercandidate signal values and the K second-layer candidate signal values.In order to prevent lengthy description, repeated and redundantdescription is omitted.

On the basis of the above, an example of the step of determining whetherto add the Q first-layer candidate signal value(s) includes: determiningwhether every n^(th) bit value of the K first-layer candidate signalvalues is identical, in which the n is an integer between 0 and (m−1)and the m is a number of bit(s) of each of the K first-layer candidatesignal values; and when determining every n^(th) bit value of the Kfirst-layer candidate signal values is identical, adding the Qfirst-layer candidate signal value(s). For instance, as shown in FIG. 8,the signal detecting circuit 150 may further include a candidate signalvalue adding unit 810 configured to determine whether to addsupplemental candidate signal value(s). An embodiment of the candidatesignal value adding unit 810 may generate a summation by summing then^(th) bit value of every candidate signal value of the K first-layercandidate signal values up and then determine whether the n^(th) bitvalue of every candidate signal value is identical according to thesummation. More specifically, when the summation is zero, it isascertained that the n^(th) bit value of every candidate signal value iszero; when the summation is K, it is ascertained that the n^(th) bitvalue of every candidate signal value of the K first-layer candidatesignal values is one. Another embodiment of the candidate signal valueadding unit 810 may compare the n^(th) bit value of every candidatesignal value with a predetermined bit value, so as to determine whetherthe n^(th) bit value of every candidate signal value is identicalaccording to whether every comparison result is identical. These andother modifications can be implemented in the candidate signal valueadding unit 810. The step of determining whether to add the P or the Rsecond-layer candidate signal value(s) can be derived from the abovedisclosure by people of ordinary skill in the art.

When determining to add the Q first-layer candidate signal value(s), thecandidate signal value adding unit 810 may add the Q first-layercandidate signal value(s) by executing the following steps: selecting afirst-layer reference candidate signal value (e.g., {tilde over(X)}_(1,Eq) in FIG. 9) from the K first-layer candidate signal valuesaccording to a zero-forcing (ZF) calculation result (as shown in Eq. 8);and adding the Q first-layer candidate signal value(s) according to thefirst-layer reference candidate signal value. For instance, the step ofadding the Q first-layer candidate signal value(s) according to thefirst-layer reference candidate signal value includes: executing alook-up-table operation, which accesses pre-stored data, according tothe first-layer reference candidate signal value and thereby adding theQ first-layer candidate signal value(s) according to the pre-storeddata, in which on a constellation diagram each of the Q first-layercandidate signal value(s) is in a horizontal or vertical direction ofthe first-layer reference candidate signal value (as shown in FIG. 10).The constellation diagram is associated with a modulation type of thereception signal, and the Q is not greater than a number of bit(s) ofthe first-layer reference candidate signal value (e.g., the number ofbits of the first-layer reference candidate signal value “010000” issix).

The step of adding the Q first-layer candidate signal value(s) isfurther explained below. Please refer to FIG. 9 and FIG. 10. Take thei^(th) bit of every {tilde over (X)}₁ being j; for instance, all themost significant bits of nine {tilde over (X)}₁ in the search range ofFIG. 9 are “0” (while bit “1” is missing in the bit set of the mostsignificant bits) and all the second significant bits of {tilde over(X)}₁ in the search range are “1” (while bit “0” is missing in the bitset of the second significant bits). According to the first-layerreference candidate signal value {tilde over (X)}_(1,Eq), obtained bythe aforementioned zero-forcing calculation, each constellation point,whose i^(th) bit is j, closest to {tilde over (X)}_(1,Eq) is found, andat least a part (i.e., the constellation point(s) not found in theexisting first-layer candidate signal values) of the said constellationpoint(s) is treated as the Q first-layer candidate value. Theabove-mentioned process in search of supplemental candidate value(s) isdependent on the design of the constellation diagram. Take FIG. 10(i.e., a constellation diagram of 64-QAM) for example. When thereal-part of {tilde over (X)}_(1,Eq) is located within the section2<Re[{tilde over (X)}_(1,Eq)]≦3 and the imaginary-part of {tilde over(X)}_(1,Eq) is located within the section −3<Im[{tilde over(X)}_(1,Eq)]≦−2 (and in this case, the bits of the constellation pointclosest to {tilde over (X)}_(1,Eq) is “010000” (i.e., the first-layerreference candidate signal value) by an order from the most significantbit to the least significant bit), the constellation point being closestto {tilde over (X)}_(1,Eq) and having the reversed value of the mostsignificant bit of “010000” is labeled as b₀, the constellation pointbeing closest to {tilde over (X)}_(1,Eq) and having the reversed valueof the second significant bit of “010000” is labeled as b₁, . . . , theconstellation point being closest to {tilde over (X)}_(1,Eq) and havingthe reversed value of the least significant bit of “010000” is labeledas b₅, and these constellation points b₀, b₁, b₂, b₃, b₄ and b₅ havingthe reversed values are eligible for supplemental candidate values(e.g., b₀ and b₁ eligible for the supplemental candidate signal valuesof FIG. 9). Such constellation points are located in a cross where theconstellation point (i.e., “010000”) closest to {tilde over (X)}_(1,Eq)is centered. The relation between the said constellation points and{tilde over (X)}_(1,Eq) is listed in Tables 3-5. Accordingly, through alook-up-table operation or an equivalent thereof, the candidate signalvalue adding unit 810 is capable of finding the constellation points,whose i^(th) bit is j, closet to the pinpoint {tilde over (X)}_(1,Eq).

TABLE 3 nearest nearest constellation point constellation point havingthe reversed having the reversed value of the most value of the secondsignificant bit significant bit Re[{tilde over (X)}_(1,Eq)] ≦ 0 (1,Im[{tilde over (X)}₁]) Im[{tilde over (X)}_(1,Eq)] ≦ 0 (Re[{tilde over(X)}₁], 1) 0 < Re[{tilde over (X)}_(1,Eq)] (−1, Im[{tilde over (X)}₁]) 0< Im[{tilde over (X)}_(1,Eq)] (Re[{tilde over (X)}₁], −1)

TABLE 4 nearest nearest constellation constellation point having pointhaving the reversed the reversed value of the value of the third fourthsignificant bit significant bit Re[{tilde over (X)}_(1,Eq)] ≦ −4 (−3,Im[{tilde over (X)}₁]) Im[{tilde over (X)}_(1,Eq)] ≦ 0 (Re[{tilde over(X)}₁], −3) −4 < Re[{tilde over (X)}_(1,Eq)] ≦ 0 (−5, Im[{tilde over(X)}₁]) −4 < Im[{tilde over (X)}_(1,Eq)] ≦ 0 (Re[{tilde over (X)}₁], −5)0 < Re[{tilde over (X)}_(1,Eq)] ≦ 4 (5, Im[{tilde over (X)}₁]) 0 <Im[{tilde over (X)}_(1,Eq)] ≦ 4 (Re[{tilde over (X)}₁], 5) 0 < Re[{tildeover (X)}_(1,Eq)] (3, Im[{tilde over (X)}₁]) 0 < Im[{tilde over(X)}_(1,Eq)] (Re[{tilde over (X)}₁], 3)

TABLE 5 nearest nearest constellation constellation point having pointhaving the reversed the reversed value of the value of the fifth leastsignificant bit significant bit Re[{tilde over (X)}_(1,Eq)] ≦ −6 (−5,Im[{tilde over (X)}₁]) Im[{tilde over (X)}_(1,Eq)] ≦ −6 (Re[{tilde over(X)}₁], −5) −6 < Re[{tilde over (X)}_(1,Eq)] ≦ −4 (−7, Im[{tilde over(X)}₁]) −6 < Im[{tilde over (X)}_(1,Eq)] ≦ −4 (Re[{tilde over (X)}₁],−7) −4 < Re[{tilde over (X)}_(1,Eq)] ≦ −2 (−1, Im[{tilde over (X)}₁]) −4< Im[{tilde over (X)}_(1,Eq)] ≦ −2 (Re[{tilde over (X)}₁], −1) −2 <Re[{tilde over (X)}_(1,Eq)] ≦ 0 (−3, Im[{tilde over (X)}₁]) −2 <Im[{tilde over (X)}_(1,Eq)] ≦ 0 (Re[{tilde over (X)}₁], −3) 0 <Re[{tilde over (X)}_(1,Eq)] ≦ 2 (3, Im[{tilde over (X)}₁]) 0 < Im[{tildeover (X)}_(1,Eq)] ≦ 2 (Re[{tilde over (X)}₁], 3) 2 < Re[{tilde over(X)}_(1,Eq)] ≦ 4 (1, Im[{tilde over (X)}₁]) 2 < Im[{tilde over(X)}_(1,Eq)] ≦ 4 (Re[{tilde over (X)}₁], 1) 4 < Re[{tilde over(X)}_(1,Eq)] ≦ 6 (7, Im[{tilde over (X)}₁]) 4 < Im[{tilde over(X)}_(1,Eq)] ≦ 6 (Re[{tilde over (X)}₁], 7) 6 < Re[{tilde over(X)}_(1,Eq)] (5, Im[{tilde over (X)}₁]) 6 < Im[{tilde over (X)}_(1,Eq)](Re[{tilde over (X)}₁], 5)

The step of adding the P or the R second-layer candidate signal value(s)is similar to the aforementioned step of adding the Q first-layercandidate signal value(s). More specifically, the first-layer referencecandidate signal value of the Q first-layer candidate signal value(s) isobtained through the zero-forcing operation, and the second-layercandidate signal value(s) corresponding to the Q first-layer candidatesignal value(s) is/are obtained according to Eq. 7; the second-layerreference candidate signal value of the P or the R second-layercandidate signal value(s) is the signal value capable of reaching aminimum difference among a plurality of differences. For instance, afirst-layer candidate signal value and its corresponding second-layercandidate signal value can be introduced into Eq. 6 to obtain adifference, and similarly K first-layer candidate signal values and thecorresponding K second-layer candidate signal value can be introducedinto Eq. 6 to obtain K differences including a minimum differencetherein. The first-layer candidate signal value(s), among the existingfirst-layer candidate signal values, corresponding to the P or the Rsecond-layer candidate signal value(s) is/are the signal value(s) thatcan be used to reach the said minimum difference. The way to add thefirst-layer candidate signal value is the same as the way to add thesecond-layer candidate signal value except the above-describeddistinction.

Please refer to FIG. 11. The ML detector 152 is operable to execute thereversed operation (which unfolds possible solutions of {tilde over(X)}₀ at the first layer and then unfolds possible solutions of {tildeover (X)}₁ at the second layer) with the aid of a swap operationdecision unit 1110. The swap operation decision unit 1110 is operable todetermine whether to execute a swap operation according to the channelestimation signal, operable to output the channel estimation signal tothe QR decomposition unit 530 when determining not to execute the swapoperation, and operable to perform the swap operation to the channelestimation signal when determining to execute the swap operation so asto output a swap signal of the channel estimation signal to the QRdecomposition unit 530. More specifically, an embodiment of the swapoperation decision unit 1310 determines a priority order of signaldetection according to the channel response matrix H of the channelestimation signal; for instance, the swap operation decision unit 1310determines the priority order according to the energy of the matrix A.In an exemplary implementation, provided that the firstly unfoldedsignal is {tilde over (X)}_(u) ({tilde over (X)}₁ or {tilde over (X)}₀)and the secondly unfolded signal is {tilde over (X)}_(ū) ({tilde over(X)}₀ or {tilde over (X)}₁), when the square of the absolute value(s) ofthe first column in the matrix H is less than the square of the absolutevalue(s) of the second column in the matrix Ĥ the swap operationdecision unit 1310 will exchange the first column of H with the secondcolumn of H so as to output the swap signal of the channel estimationsignal and thereby make the firstly unfolded signal be {tilde over(X)}₀. When the square of the absolute value(s) of the first column inthe matrix Ĥ is greater than the square of the absolute value(s) of thesecond column in the matrix Ĥ, the swap operation decision unit 1310will output the matrix Ĥ (i.e., the channel estimation signal) andthereby make the firstly unfolded signal be {tilde over (X)}₁. In brief,the swap operation decision unit 1310 is operable to make the signal oflower energy be detected first, so as to make the aforementionedfirst-layer search value greater and thus decrease the probability ofmissing some necessary candidate signal value(s). However, if the signaldetecting circuit 150 always detects {tilde over (X)}₁ firstly or anequivalent of the signal detecting circuit 150 always detects {tildeover (X)}₀ firstly, the swap operation decision unit 1110 could beomitted.

Please refer to FIG. 12. FIG. 12 illustrates another embodiment of thesignal detecting circuit 150. In this embodiment, the signal detectingcircuit 150 executes the aforementioned straight operation with a firstdetecting circuit 1210 and executes the reversed operation with a seconddetecting circuit 1220. An embodiment of the first detecting circuit1210 is shown in FIG. 13 and an embodiment of the second detectingcircuit 1220 is shown in FIG. 14, in which the candidate signal valueadding unit 810 is configured to examine the first-layer candidatesignal values {tilde over (X)}_(u) of the straight operation and examinethe first-layer candidate signal values {tilde over (X)}_(ū) of thereversed operation so as to find out whether some candidate signalvalue(s) is missing in the signal value sets of {tilde over (X)}_(u) and{tilde over (X)}_(ū) without the need of checking the second-layercandidate signal values. Of course the above-described manner isexemplary rather than restrictive. Please refer to FIG. 14, the swapunit 1410 will exchange the first column of the channel response matrixĤ with the second column of the matrix Ĥ so as to output the swap signalof the channel estimation signal. It should be noted that thefirst-layer search value generated by the first detecting circuit 1210may be equal to or different from the first-layer search value generatedby the second detecting circuit 1220. For instance, the first-layersearch values generated by the first and second detecting circuits 1210,1220 respectively are K₁, K₂, in which each of the K₁, K₂ is generatedaccording to a communication index.

Since those of ordinary skill in the art can appreciate the detail andmodification of the embodiments of FIGS. 12-14 by referring to theexplanation of the embodiments described in the preceding paragraphs,repeated and redundant description is omitted without failing thewritten description and enablement requirements. It should be noted thatthere is no specific order for executing a plurality of steps in eachembodiment of the present disclosure as long as such execution of thesteps is practicable; in addition, one step could be composed ofmultiple sub-steps; these and other features can be found in or derivedfrom the disclosure by those of ordinary skill in the art.

In summary, the present invention is not complicated, and can achievelow latency, low computation complexity, low computation powerconsumption, small circuit size and no performance loss in comparisonwith the prior arts.

The aforementioned descriptions represent merely the preferredembodiments of the present invention, without any intention to limit thescope of the present invention thereto. Various equivalent changes,alterations, or modifications based on the claims of present inventionare all consequently viewed as being embraced by the scope of thepresent invention.

What is claimed is:
 1. A wireless signal receiver with a maximumlikelihood detector (ML detector), the wireless signal receivercomprising: a Discrete Fourier Transform (DFT) circuit configured toconvert a time-domain signal into a frequency-domain signal; a referencesignal extraction circuit configured to generate an extraction referencesignal according to the frequency-domain signal; a channel estimationcircuit configured to generate a channel estimation signal according tothe extraction reference signal; a data signal extraction circuitconfigured to generate a reception signal according to thefrequency-domain signal; the ML detector configured to generate adetection signal according to one of the channel estimation signal and aderivative thereof and according to one of the reception signal and aderivative thereof; and a decoding circuit configured to generate adecoded signal according to the detection signal, wherein the MLdetector includes: a search value selecting circuit configured todetermine a first-layer search value; and a maximum likelihood detectingcircuit (ML detecting circuit) configured to execute at least thefollowing steps: selecting K first-layer candidate signal valuesaccording to the first-layer search value, one of the reception signaland the derivative thereof, and one of the channel estimation signal andthe derivative thereof, in which the K is an integer greater than one;determining whether to add Q first-layer candidate signal value(s)according to the K first-layer candidate signal values and therebygenerating a first decision result, in which the Q is a positiveinteger; when the first decision result is affirmative, adding the Qfirst-layer candidate signal value(s), and calculating (K+Q)second-layer candidate signal values according to (K+Q) first-layercandidate signal values composed of the K and the Q first-layercandidate signal values; when the first decision result is affirmative,determining whether to add P second-layer candidate signal value(s)according to the (K+Q) second-layer candidate signal values and therebygenerating a second decision result, in which the P is a positiveinteger; when the first and the second decision results are affirmative,adding the P second-layer candidate signal value(s), selecting Pfirst-layer candidate signal value(s) according to the P second-layercandidate signal value(s), and calculating log likelihood ratios (LLRs)according to (K+Q+P) first-layer candidate signal values composed of theK, the Q and the P first-layer candidate signal values and according to(K+Q+P) second-layer candidate signal values composed of the (K+Q) the Psecond-layer candidate signal values; when the first decision result isaffirmative and the second decision result is negative, calculating LLRsaccording to the (K+Q) first-layer candidate signal values and the (K+Q)second-layer candidate signal values; when the first decision result isnegative, calculating K second-layer candidate signal values accordingthe K first-layer candidate signal values; when the first decisionresult is negative, determining whether to add R second-layer candidatesignal value(s) according to the K second-layer candidate signal valuesand thereby generating a third decision result, in which the R is apositive integer; when the first decision result is negative and thethird decision result is affirmative, adding the R second-layercandidate signal value(s), selecting R first-layer candidate signalvalue(s) according to the R second-layer candidate signal value(s), andcalculating LLRs according to (K+R) first-layer candidate signal valuescomposed of the K and the R first-layer candidate signal values andaccording to (K+R) second-layer candidate signal values composed of theK and the R second-layer candidate signal values; and when the first andthe third decision results are negative, calculating LLRs according tothe K first-layer candidate signal values and the K second-layercandidate signal values.
 2. The ML detector of claim 1, wherein the stepof determining whether to add the Q first-layer candidate signalvalue(s) includes: determining whether every n^(th) bit value of the Kfirst-layer candidate signal values is identical according to the Kfirst-layer candidate signal values, in which the n is an integerbetween 0 and (m−1) while the m is a number of bit(s) of each of the Kfirst-layer candidate signal values; and when determining every n^(th)bit value of the K first-layer candidate signal values is identical,adding the Q first-layer candidate signal value(s).
 3. The ML detectorof claim 1, wherein the step of determining whether to add the Psecond-layer candidate signal value(s) includes: determining whetherevery n^(th) bit value of the (K+Q) second-layer candidate signal valuesis identical according to the (K+Q) second-layer candidate signalvalues, in which the n is an integer between 0 and (m−1) while the m isa number of bit(s) of each of the (K+Q) second-layer candidate signalvalues; and when determining every n^(th) bit value of the (K+Q)second-layer candidate signal values is identical, adding the Psecond-layer candidate signal value(s).
 4. The ML detector of claim 1,wherein the step of determining whether to add the R second-layercandidate signal value(s) includes: determining whether every n^(th) bitvalue of the K second-layer candidate signal values is identicalaccording to the K second-layer candidate signal values, in which the nis an integer between 0 and (m−1) while the m is a number of bit(s) ofeach of the K second-layer candidate signal values; and when determiningevery n^(th) bit value of the K second-layer candidate signal values isidentical, adding the R second-layer candidate signal value(s).
 5. TheML detector of claim 1, wherein the step of adding the Q first-layercandidate signal value(s) includes: selecting a first-layer referencecandidate signal value from the K first-layer candidate signal valuesaccording to a zero-forcing (ZF) calculation result; and adding the Qfirst-layer candidate signal value(s) according to the first-layerreference candidate signal value.
 6. The ML detector of claim 5, whereinthe step of adding the Q first-layer candidate signal value(s) accordingto the first-layer reference candidate signal value includes: executinga look-up-table operation according to the first-layer referencecandidate signal value to access pre-stored data, and thereby adding theQ first-layer candidate signal value(s) according to the pre-storeddata.
 7. The ML detector of claim 5, wherein the Q is not greater than anumber of bit(s) of each of the K first-layer candidate signal values,or on a constellation diagram associated with a modulation type of thereception signal each of the Q first-layer candidate signal value(s) isin a horizontal or vertical direction of the first-layer referencecandidate signal value.
 8. The ML detector of claim 1, wherein the stepof adding the P second-layer candidate signal value(s) and the step ofselecting the P first-layer candidate signal value(s) include:calculating (K+Q) differences according to the (K+Q) first-layercandidate signal values and the (K+Q) second-layer candidate signalvalues; selecting a second-layer reference candidate signal value fromthe (K+Q) second-layer candidate signal values according to the (K+Q)differences, in which the second-layer reference candidate signal valueis associated with a first-layer reference candidate signal value amongthe (K+Q) first-layer candidate signal values; adding the P second-layercandidate signal value(s) according to the second-layer referencecandidate signal value; and making each of the P first-layer candidatesignal value(s) equal to the first-layer reference candidate signalvalue.
 9. The ML detector of claim 8, wherein the step of selecting thesecond-layer reference candidate signal value includes: selecting thesecond-layer reference candidate signal value from the (K+Q)second-layer candidate signal values according to a minimum of the (K+Q)differences.
 10. The ML detector of claim 8, wherein the step of addingthe P second-layer candidate signal value(s) according to thesecond-layer reference candidate signal value includes: executing alook-up-table operation according to the second-layer referencecandidate signal value to access pre-stored data, and thereby adding theP second-layer candidate signal value(s) according to the pre-storeddata.
 11. The ML detector of claim 8, wherein the P is not greater thana number of bit(s) of each of the (K+Q) second-layer candidate signalvalues, or on a constellation diagram associated with a modulation typeof the reception signal each of the P second-layer candidate signalvalue(s) is in a horizontal or vertical direction of the second-layerreference candidate signal value.
 12. The ML detector of claim 1,wherein the step of adding the R second-layer candidate signal value(s)and the step of selecting the R first-layer candidate signal value(s)include: calculating K differences according to the K first-layercandidate signal values and the K second-layer candidate signal values;selecting a second-layer reference candidate signal value from the Ksecond-layer candidate signal values according to the K differences, inwhich the second-layer reference candidate signal value is associatedwith a first-layer reference candidate signal value among the Kfirst-layer candidate signal values; adding the R second-layer candidatesignal value(s) according to the second-layer reference candidate signalvalue; and making each of the R first-layer candidate signal value(s)equal to the first-layer reference candidate signal value.
 13. The MLdetector of claim 12, wherein the step of selecting the second-layerreference candidate signal value includes: selecting the second-layerreference candidate signal value from the K second-layer candidatesignal values according to a minimum of the K differences.
 14. The MLdetector of claim 12, wherein the step of adding the R second-layercandidate signal value(s) according to the second-layer referencecandidate signal value includes: executing a look-up-table operationaccording to the second-layer reference candidate signal value to accesspre-stored data, and thereby adding the R second-layer candidate signalvalue(s) according to the pre-stored data.
 15. A wireless signalreceiver with a maximum likelihood detector (ML detector), the wirelesssignal receiver comprising: a Discrete Fourier Transform (DFT) circuitconfigured to convert a time-domain signal into a frequency-domainsignal; a reference signal extraction circuit configured to generate anextraction reference signal according to the frequency-domain signal; achannel estimation circuit configured to generate a channel estimationsignal according to the extraction reference signal; a data signalextraction circuit configured to generate a reception signal accordingto the frequency-domain signal; the ML detector configured to generate adetection signal according to one of the channel estimation signal and aderivative thereof and according to one of the reception signal and aderivative thereof, the ML detector including: a search value selectingcircuit configured to determine a first-layer search value; and amaximum likelihood detecting circuit (ML detecting circuit) configuredto calculate log likelihood ratios (LLRs) according to first-layercandidate values and second-layer candidate values, in which the LLRsare included in the detection signal, the first-layer candidate valuesare selected according to the first-layer search value, one of thereception signal and the derivative thereof, and one of the channelestimation signal and the derivative thereof, and the second-layercandidate values are calculated according to the first-layer candidatevalues; and a decoding circuit configured to generate a decoded signalaccording to the detection signal, wherein a modulation type of thereception signal is quadrature amplitude modulation with a signal set ofsize M, and the first-layer search value is an integer less than M. 16.A wireless signal receiver with a maximum likelihood detector (MLdetector), the wireless signal receiver comprising: a Discrete FourierTransform (DFT) circuit configured to convert a time-domain signal intoa frequency-domain signal; a reference signal extraction circuitconfigured to generate an extraction reference signal according to thefrequency-domain signal; a channel estimation circuit configured togenerate a channel estimation signal according to the extractionreference signal; a data signal extraction circuit configured togenerate a reception signal according to the frequency-domain signal;the ML detector configured to generate a detection signal according toone of the channel estimation signal and a derivative thereof andaccording to one of the reception signal and a derivative thereof; and adecoding circuit configured to generate a decoded signal according tothe detection signal, wherein the ML detector includes: a search valueselecting circuit configured to determine a first search value and asecond search value; and a maximum likelihood detecting circuit (MLdetecting circuit) configured to execute at least the following steps:selecting K₁ first-layer candidate signal values according to the firstsearch value, one of the reception signal and the derivative thereof,and one of the channel estimation signal and the derivative thereof, inwhich the K₁ is an integer greater than one; determining whether to addQ first-layer candidate signal value(s) according to the K₁ first-layercandidate signal values and thereby generating a first decision result,in which the Q is a positive integer; when the first decision result isaffirmative, adding the Q first-layer candidate signal value(s), andcalculating (K₁+Q) second-layer candidate signal values according to(K₁+Q) first-layer candidate signal values composed of the K₁ and the Qfirst-layer candidate signal values; when the first decision result isaffirmative, calculating log likelihood ratios (LLRs) according to the(K₁+Q) first-layer candidate signal values and the (K₁+Q) second-layercandidate signal values; when the first decision result is negative,calculating K₁ second-layer candidate signal values according to the K₁first-layer candidate signal values and calculating LLRs according tothe K₁ first-layer candidate signal values and K₁ second-layer candidatesignal values; selecting K₂ first-layer candidate signal valuesaccording to the second search value, one of the reception signal andthe derivative thereof, and one of a swap signal of the channelestimation signal and a derivative of the swap signal, in which the K₂is an integer greater than one; determining whether to add P first-layercandidate signal value(s) according to the K₂ first-layer candidatesignal values and thereby generating a second decision result, in whichthe P is a positive integer; when the second decision result isaffirmative, adding the P first-layer candidate signal value(s), andcalculating (K₂+P) second-layer candidate signal values according to(K₂+P) first-layer candidate signal values composed of the K₂ and the Pfirst-layer candidate signal values; when the second decision result isaffirmative, calculating LLRs according to the (K₂+P) first-layercandidate signal values and the (K₂+P) second-layer candidate signalvalues; when the second decision result is negative, calculating K₂second-layer candidate signal values according to the K₂ first-layercandidate signal values and calculating LLRs according to the K₂first-layer candidate signal values and K₂ second-layer candidate signalvalues.
 17. The ML detector of claim 16, wherein the step of determiningwhether to add the Q first-layer candidate signal value(s) includes:determining whether every n^(th) bit value of the K₁ first-layercandidate signal values is identical according to the K₁ first-layercandidate signal values, in which the n is an integer between 0 and(m−1) while the m is a number of bit(s) of each of the K₁ first-layercandidate signal values; and when determining every n^(th) bit value ofthe K₁ first-layer candidate signal values is identical, adding the Qfirst-layer candidate signal value(s).
 18. The ML detector of claim 16,wherein the step of determining whether to add the P first-layercandidate signal value(s) includes: determining whether every n^(th) bitvalue of the K₂ first-layer candidate signal values is identicalaccording to the K₂ first-layer candidate signal values, in which the nis an integer between 0 and (m−1) while the m is a number of bit(s) ofeach of the K₂ first-layer candidate signal values; and when determiningevery n^(th) bit value of the K₂ first-layer candidate signal values isidentical, adding the P first-layer candidate signal value(s).
 19. TheML detector of claim 16, wherein the step of adding the Q first-layercandidate signal value(s) includes: selecting a first-layer referencecandidate signal value from the K₁ first-layer candidate signal valuesaccording to a zero-forcing (ZF) calculation result; and adding the Qfirst-layer candidate signal value(s) according to the first-layerreference candidate signal value.
 20. The ML detector of claim 16,wherein the step of adding the P first-layer candidate signal value(s)includes: selecting a first-layer reference candidate signal value fromthe K₂ first-layer candidate signal values according to a zero-forcing(ZF) calculation result; and adding the P first-layer candidate signalvalue(s) according to the first-layer reference candidate signal value.